--- title: "agentdojo vs dingo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-migoxlab-dingo" tools: ["ethz-spylab-agentdojo", "migoxlab-dingo"] --- # agentdojo vs dingo *GraphCanon updated Aug 7, 2026* ## Verdict Pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents; pick dingo if dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks. [agentdojo](https://agentdojo.spylab.ai/) reports 716 GitHub stars, 188 forks, and 41 open issues, last pushed Jun 2, 2026. [dingo](https://dingo.openxlab.org.cn/) has 733 stars, 74 forks, and 4 open issues, last pushed Aug 6, 2026. Figures are from public GitHub metadata via [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [dingo's repository](https://github.com/MigoXLab/dingo). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [dingo](/tools/migoxlab-dingo.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool | | Stars | 716 | 733 | | Forks | 188 | 74 | | Open issues | 41 | 4 | | Language | Python | Python | | Adopt for | AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. | Dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks. | | Persona | - | - | | Runtime | - | - | | License | MIT | Licensed under the Apache-2.0 license, it includes fasttext functionality for language detection, which itself is licensed under the MIT License. | | Categories | AI Agents, Evaluation & Observability | Data & Retrieval, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [dingo](/tools/migoxlab-dingo.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 63d | 0d | | Open issues (now) | 41 | 4 | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/migoxlab-dingo/trust.md) | ## Decision facts: agentdojo - **Pricing:** freemium - Open-source under the MIT License. Some advanced features might require additional libraries or APIs. - **Requirements:** Min 8 GB RAM - **Adopt for:** AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ## Decision facts: dingo - **Pricing:** freemium - The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost. - **Adopt for:** Dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks. - **License detail:** Licensed under the Apache-2.0 license, it includes fasttext functionality for language detection, which itself is licensed under the MIT License. ## Choose when ### Choose agentdojo if… - License: agentdojo is MIT, dingo is Apache-2.0. - Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs.. - Requirements: Min 8 GB RAM. - Tags unique to agentdojo: benchmark, large language models, prompt-injection, security. - Also covers AI Agents. - AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ### Choose dingo if… - License: dingo is Apache-2.0, agentdojo is MIT. - Pricing: The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost.. - Tags unique to dingo: agent-as-a-judge, data-evaluation, data-quality, hallucination-detection. - Also covers Data & Retrieval. - When evaluating the quality of data, models, or applications that require insights from multiple perspectives to detect nuances such as bias or hallucination. ## When NOT to use agentdojo - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use dingo - If your project does not benefit from a multi-agent approach for evaluation, and simpler single-model approaches suffice. - In scenarios where immediate feedback is critical but Dingo's planned SaaS platform with API access and dashboard support are still under development. ## Common questions ### What is the difference between agentdojo and dingo? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. dingo: Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentdojo over dingo? Choose agentdojo over dingo when License: agentdojo is MIT, dingo is Apache-2.0; Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs.; Requirements: Min 8 GB RAM; Tags unique to agentdojo: benchmark, large language models, prompt-injection, security; Also covers AI Agents; AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ### When should I choose dingo over agentdojo? Choose dingo over agentdojo when License: dingo is Apache-2.0, agentdojo is MIT; Pricing: The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost.; Tags unique to dingo: agent-as-a-judge, data-evaluation, data-quality, hallucination-detection; Also covers Data & Retrieval; When evaluating the quality of data, models, or applications that require insights from multiple perspectives to detect nuances such as bias or hallucination. ### When should I avoid agentdojo? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid dingo? If your project does not benefit from a multi-agent approach for evaluation, and simpler single-model approaches suffice. In scenarios where immediate feedback is critical but Dingo's planned SaaS platform with API access and dashboard support are still under development. ### Is agentdojo or dingo more popular on GitHub? dingo has more GitHub stars (733 vs 716). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and dingo open source? Yes - both are open-source projects on GitHub (agentdojo: MIT, dingo: Apache-2.0). ### Where can I find alternatives to agentdojo or dingo? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [dingo alternatives](/tools/migoxlab-dingo/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/alternatives.md), [dingo markdown twin](/tools/migoxlab-dingo/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/ethz-spylab-agentdojo-vs-migoxlab-dingo.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentdojo or dingo? agentdojo: Steady. dingo: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for agentdojo and dingo? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [dingo trust report](/tools/migoxlab-dingo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ethz-spylab-agentdojo`](/api/graphcanon/graph?tool=ethz-spylab-agentdojo) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_